Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations45584
Missing cells9131
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.0 MiB
Average record size in memory781.6 B

Variable types

Text4
Numeric8
Categorical8
Boolean1

Alerts

Delivery_location_latitude is highly overall correlated with Restaurant_latitudeHigh correlation
Delivery_location_longitude is highly overall correlated with Restaurant_longitudeHigh correlation
Delivery_person_Age is highly overall correlated with Vehicle_conditionHigh correlation
Delivery_person_Ratings is highly overall correlated with Vehicle_conditionHigh correlation
Restaurant_latitude is highly overall correlated with Delivery_location_latitudeHigh correlation
Restaurant_longitude is highly overall correlated with Delivery_location_longitudeHigh correlation
Vehicle_condition is highly overall correlated with Delivery_person_Age and 1 other fieldsHigh correlation
Festival is highly imbalanced (86.0%) Imbalance
Delivery_person_Age has 1854 (4.1%) missing values Missing
Delivery_person_Ratings has 1908 (4.2%) missing values Missing
Time_Orderd has 1731 (3.8%) missing values Missing
Weather_conditions has 616 (1.4%) missing values Missing
Road_traffic_density has 601 (1.3%) missing values Missing
multiple_deliveries has 993 (2.2%) missing values Missing
City has 1200 (2.6%) missing values Missing
ID has unique values Unique
Restaurant_latitude has 3640 (8.0%) zeros Zeros
Restaurant_longitude has 3640 (8.0%) zeros Zeros

Reproduction

Analysis started2025-08-12 17:39:26.072027
Analysis finished2025-08-12 17:39:32.328439
Duration6.26 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

ID
Text

Unique 

Distinct45584
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2025-08-12T23:09:32.480927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.9457704
Min length5

Characters and Unicode

Total characters271032
Distinct characters17
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45584 ?
Unique (%)100.0%

Sample

1st row0xcdcd
2nd row0xd987
3rd row0x2784
4th row0xc8b6
5th row0xdb64
ValueCountFrequency (%)
0xcdcd 1
 
< 0.1%
0xc1ff 1
 
< 0.1%
0xbff 1
 
< 0.1%
0x36b8 1
 
< 0.1%
0x2784 1
 
< 0.1%
0xc8b6 1
 
< 0.1%
0xdb64 1
 
< 0.1%
0x3af3 1
 
< 0.1%
0x3aab 1
 
< 0.1%
0x689b 1
 
< 0.1%
Other values (45574) 45574
> 99.9%
2025-08-12T23:09:32.656266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 54117
20.0%
x 45584
16.8%
b 11990
 
4.4%
4 11887
 
4.4%
7 11882
 
4.4%
6 11871
 
4.4%
a 11862
 
4.4%
1 11822
 
4.4%
c 11816
 
4.4%
8 11812
 
4.4%
Other values (7) 76389
28.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 160481
59.2%
Lowercase Letter 110551
40.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54117
33.7%
4 11887
 
7.4%
7 11882
 
7.4%
6 11871
 
7.4%
1 11822
 
7.4%
8 11812
 
7.4%
5 11811
 
7.4%
2 11809
 
7.4%
9 11783
 
7.3%
3 11687
 
7.3%
Lowercase Letter
ValueCountFrequency (%)
x 45584
41.2%
b 11990
 
10.8%
a 11862
 
10.7%
c 11816
 
10.7%
d 11794
 
10.7%
e 8989
 
8.1%
f 8516
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Common 160481
59.2%
Latin 110551
40.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54117
33.7%
4 11887
 
7.4%
7 11882
 
7.4%
6 11871
 
7.4%
1 11822
 
7.4%
8 11812
 
7.4%
5 11811
 
7.4%
2 11809
 
7.4%
9 11783
 
7.3%
3 11687
 
7.3%
Latin
ValueCountFrequency (%)
x 45584
41.2%
b 11990
 
10.8%
a 11862
 
10.7%
c 11816
 
10.7%
d 11794
 
10.7%
e 8989
 
8.1%
f 8516
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 271032
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54117
20.0%
x 45584
16.8%
b 11990
 
4.4%
4 11887
 
4.4%
7 11882
 
4.4%
6 11871
 
4.4%
a 11862
 
4.4%
1 11822
 
4.4%
c 11816
 
4.4%
8 11812
 
4.4%
Other values (7) 76389
28.2%
Distinct1320
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2025-08-12T23:09:32.746337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length13
Mean length13.710293
Min length13

Characters and Unicode

Total characters624970
Distinct characters30
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDEHRES17DEL01
2nd rowKOCRES16DEL01
3rd rowPUNERES13DEL03
4th rowLUDHRES15DEL02
5th rowKNPRES14DEL02
ValueCountFrequency (%)
japres11del02 67
 
0.1%
puneres01del01 67
 
0.1%
vadres11del02 66
 
0.1%
vadres08del02 66
 
0.1%
japres03del01 66
 
0.1%
ranchires02del01 66
 
0.1%
hydres04del02 66
 
0.1%
indores15del01 65
 
0.1%
bangres07del02 65
 
0.1%
bangres03del01 65
 
0.1%
Other values (1310) 44925
98.6%
2025-08-12T23:09:32.874373image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 98181
15.7%
0 72931
11.7%
D 56582
 
9.1%
R 53465
 
8.6%
S 51941
 
8.3%
L 47782
 
7.6%
1 43735
 
7.0%
2 23295
 
3.7%
3 17332
 
2.8%
N 16595
 
2.7%
Other values (20) 143131
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 440366
70.5%
Decimal Number 184604
29.5%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 98181
22.3%
D 56582
12.8%
R 53465
12.1%
S 51941
11.8%
L 47782
10.9%
N 16595
 
3.8%
A 15945
 
3.6%
M 12685
 
2.9%
H 12477
 
2.8%
U 10953
 
2.5%
Other values (10) 63760
14.5%
Decimal Number
ValueCountFrequency (%)
0 72931
39.5%
1 43735
23.7%
2 23295
 
12.6%
3 17332
 
9.4%
5 4572
 
2.5%
6 4568
 
2.5%
7 4563
 
2.5%
4 4557
 
2.5%
9 4550
 
2.5%
8 4501
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 440366
70.5%
Common 184604
29.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 98181
22.3%
D 56582
12.8%
R 53465
12.1%
S 51941
11.8%
L 47782
10.9%
N 16595
 
3.8%
A 15945
 
3.6%
M 12685
 
2.9%
H 12477
 
2.8%
U 10953
 
2.5%
Other values (10) 63760
14.5%
Common
ValueCountFrequency (%)
0 72931
39.5%
1 43735
23.7%
2 23295
 
12.6%
3 17332
 
9.4%
5 4572
 
2.5%
6 4568
 
2.5%
7 4563
 
2.5%
4 4557
 
2.5%
9 4550
 
2.5%
8 4501
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 624970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 98181
15.7%
0 72931
11.7%
D 56582
 
9.1%
R 53465
 
8.6%
S 51941
 
8.3%
L 47782
 
7.6%
1 43735
 
7.0%
2 23295
 
3.7%
3 17332
 
2.8%
N 16595
 
2.7%
Other values (20) 143131
22.9%

Delivery_person_Age
Real number (ℝ)

High correlation  Missing 

Distinct22
Distinct (%)0.1%
Missing1854
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean29.566911
Minimum15
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-08-12T23:09:32.924127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile21
Q125
median30
Q335
95-th percentile39
Maximum50
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.8150639
Coefficient of variation (CV)0.19667472
Kurtosis-1.058167
Mean29.566911
Median Absolute Deviation (MAD)5
Skewness0.018771873
Sum1292961
Variance33.814968
MonotonicityNot monotonic
2025-08-12T23:09:32.965818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
35 2261
 
5.0%
36 2260
 
5.0%
30 2226
 
4.9%
37 2226
 
4.9%
38 2218
 
4.9%
24 2210
 
4.8%
32 2201
 
4.8%
22 2194
 
4.8%
29 2191
 
4.8%
33 2186
 
4.8%
Other values (12) 21557
47.3%
ValueCountFrequency (%)
15 38
 
0.1%
20 2136
4.7%
21 2153
4.7%
22 2194
4.8%
23 2086
4.6%
24 2210
4.8%
25 2174
4.8%
26 2159
4.7%
27 2150
4.7%
28 2179
4.8%
ValueCountFrequency (%)
50 53
 
0.1%
39 2144
4.7%
38 2218
4.9%
37 2226
4.9%
36 2260
5.0%
35 2261
5.0%
34 2165
4.7%
33 2186
4.8%
32 2201
4.8%
31 2120
4.7%

Delivery_person_Ratings
Real number (ℝ)

High correlation  Missing 

Distinct28
Distinct (%)0.1%
Missing1908
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean4.6337737
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-08-12T23:09:33.117154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q14.5
median4.7
Q34.9
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.33474372
Coefficient of variation (CV)0.07223998
Kurtosis15.668238
Mean4.6337737
Median Absolute Deviation (MAD)0.2
Skewness-2.4934025
Sum202384.7
Variance0.11205336
MonotonicityNot monotonic
2025-08-12T23:09:33.165211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4.8 7146
15.7%
4.7 7140
15.7%
4.9 7040
15.4%
4.6 6938
15.2%
5 3996
8.8%
4.5 3302
7.2%
4.1 1430
 
3.1%
4.2 1418
 
3.1%
4.3 1409
 
3.1%
4.4 1360
 
3.0%
Other values (18) 2497
 
5.5%
(Missing) 1908
 
4.2%
ValueCountFrequency (%)
1 38
0.1%
2.5 20
< 0.1%
2.6 22
< 0.1%
2.7 22
< 0.1%
2.8 19
< 0.1%
2.9 19
< 0.1%
3 6
 
< 0.1%
3.1 29
0.1%
3.2 29
0.1%
3.3 25
0.1%
ValueCountFrequency (%)
6 53
 
0.1%
5 3996
8.8%
4.9 7040
15.4%
4.8 7146
15.7%
4.7 7140
15.7%
4.6 6938
15.2%
4.5 3302
7.2%
4.4 1360
 
3.0%
4.3 1409
 
3.1%
4.2 1418
 
3.1%

Restaurant_latitude
Real number (ℝ)

High correlation  Zeros 

Distinct657
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.017948
Minimum-30.905562
Maximum30.914057
Zeros3640
Zeros (%)8.0%
Negative431
Negative (%)0.9%
Memory size356.3 KiB
2025-08-12T23:09:33.216495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-30.905562
5-th percentile0
Q112.933284
median18.55144
Q322.728163
95-th percentile26.913987
Maximum30.914057
Range61.819619
Interquartile range (IQR)9.794879

Descriptive statistics

Standard deviation8.1856738
Coefficient of variation (CV)0.48100239
Kurtosis3.7133088
Mean17.017948
Median Absolute Deviation (MAD)5.487259
Skewness-1.3616475
Sum775746.16
Variance67.005255
MonotonicityNot monotonic
2025-08-12T23:09:33.267335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3640
 
8.0%
26.911378 182
 
0.4%
26.914142 180
 
0.4%
26.902908 176
 
0.4%
26.90294 176
 
0.4%
26.892312 176
 
0.4%
26.88842 174
 
0.4%
26.913726 173
 
0.4%
26.905287 173
 
0.4%
22.308096 172
 
0.4%
Other values (647) 40362
88.5%
ValueCountFrequency (%)
-30.905562 1
 
< 0.1%
-30.902872 2
< 0.1%
-30.899584 3
< 0.1%
-30.895817 3
< 0.1%
-30.893384 1
 
< 0.1%
-30.893244 1
 
< 0.1%
-30.892978 1
 
< 0.1%
-30.890184 1
 
< 0.1%
-30.885915 1
 
< 0.1%
-30.885814 1
 
< 0.1%
ValueCountFrequency (%)
30.914057 42
0.1%
30.905562 37
0.1%
30.902872 32
0.1%
30.899992 38
0.1%
30.899584 41
0.1%
30.895817 36
0.1%
30.895204 41
0.1%
30.893384 38
0.1%
30.893244 38
0.1%
30.893234 39
0.1%

Restaurant_longitude
Real number (ℝ)

High correlation  Zeros 

Distinct518
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.229684
Minimum-88.366217
Maximum88.433452
Zeros3640
Zeros (%)8.0%
Negative162
Negative (%)0.4%
Memory size356.3 KiB
2025-08-12T23:09:33.305954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-88.366217
5-th percentile0
Q173.17
median75.897963
Q378.044095
95-th percentile85.325347
Maximum88.433452
Range176.79967
Interquartile range (IQR)4.874095

Descriptive statistics

Standard deviation22.885575
Coefficient of variation (CV)0.32586755
Kurtosis10.300249
Mean70.229684
Median Absolute Deviation (MAD)2.162258
Skewness-3.2197783
Sum3201349.9
Variance523.74954
MonotonicityNot monotonic
2025-08-12T23:09:33.369663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3640
 
8.0%
75.789034 182
 
0.4%
75.805704 181
 
0.4%
75.793007 177
 
0.4%
75.806896 176
 
0.4%
75.792934 176
 
0.4%
75.75282 174
 
0.4%
75.800689 174
 
0.4%
75.794592 173
 
0.4%
73.167753 173
 
0.4%
Other values (508) 40358
88.5%
ValueCountFrequency (%)
-88.366217 1
 
< 0.1%
-88.352885 1
 
< 0.1%
-88.349843 1
 
< 0.1%
-88.322337 1
 
< 0.1%
-85.33982 1
 
< 0.1%
-85.335486 1
 
< 0.1%
-85.325731 3
< 0.1%
-85.325447 2
< 0.1%
-85.325146 1
 
< 0.1%
-85.3172 1
 
< 0.1%
ValueCountFrequency (%)
88.433452 35
0.1%
88.433187 36
0.1%
88.400581 34
0.1%
88.400467 33
0.1%
88.39331 36
0.1%
88.393294 38
0.1%
88.368628 35
0.1%
88.36783 33
0.1%
88.366217 33
0.1%
88.365507 37
0.1%

Delivery_location_latitude
Real number (ℝ)

High correlation 

Distinct4373
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.46548
Minimum0.01
Maximum31.054057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-08-12T23:09:33.417262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q112.988453
median18.633934
Q322.785049
95-th percentile27.023726
Maximum31.054057
Range31.044057
Interquartile range (IQR)9.796596

Descriptive statistics

Standard deviation7.3355617
Coefficient of variation (CV)0.42000344
Kurtosis0.26427763
Mean17.46548
Median Absolute Deviation (MAD)5.4788475
Skewness-0.70118297
Sum796146.43
Variance53.810465
MonotonicityNot monotonic
2025-08-12T23:09:33.486800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 341
 
0.7%
0.02 337
 
0.7%
0.09 336
 
0.7%
0.06 336
 
0.7%
0.07 335
 
0.7%
0.04 335
 
0.7%
0.05 328
 
0.7%
0.11 328
 
0.7%
0.01 327
 
0.7%
0.08 324
 
0.7%
Other values (4363) 42257
92.7%
ValueCountFrequency (%)
0.01 327
0.7%
0.02 337
0.7%
0.03 313
0.7%
0.04 335
0.7%
0.05 328
0.7%
0.06 336
0.7%
0.07 335
0.7%
0.08 324
0.7%
0.09 336
0.7%
0.11 328
0.7%
ValueCountFrequency (%)
31.054057 3
< 0.1%
31.045562 4
< 0.1%
31.044057 4
< 0.1%
31.042872 2
< 0.1%
31.039992 3
< 0.1%
31.039584 4
< 0.1%
31.035817 4
< 0.1%
31.035562 3
< 0.1%
31.035204 4
< 0.1%
31.033384 4
< 0.1%

Delivery_location_longitude
Real number (ℝ)

High correlation 

Distinct4373
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.844161
Minimum0.01
Maximum88.563452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-08-12T23:09:33.538004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.07
Q173.28
median76.002574
Q378.107044
95-th percentile85.375486
Maximum88.563452
Range88.553452
Interquartile range (IQR)4.827044

Descriptive statistics

Standard deviation21.120578
Coefficient of variation (CV)0.2981273
Kurtosis7.1021951
Mean70.844161
Median Absolute Deviation (MAD)2.196673
Skewness-2.956022
Sum3229360.2
Variance446.07883
MonotonicityNot monotonic
2025-08-12T23:09:33.590240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.13 341
 
0.7%
0.02 337
 
0.7%
0.09 336
 
0.7%
0.06 336
 
0.7%
0.07 335
 
0.7%
0.04 335
 
0.7%
0.05 328
 
0.7%
0.11 328
 
0.7%
0.01 327
 
0.7%
0.08 324
 
0.7%
Other values (4363) 42257
92.7%
ValueCountFrequency (%)
0.01 327
0.7%
0.02 337
0.7%
0.03 313
0.7%
0.04 335
0.7%
0.05 328
0.7%
0.06 336
0.7%
0.07 335
0.7%
0.08 324
0.7%
0.09 336
0.7%
0.11 328
0.7%
ValueCountFrequency (%)
88.563452 2
< 0.1%
88.563187 4
< 0.1%
88.543452 3
< 0.1%
88.543187 4
< 0.1%
88.530581 4
< 0.1%
88.530467 3
< 0.1%
88.523452 4
< 0.1%
88.52331 4
< 0.1%
88.523294 2
< 0.1%
88.523187 2
< 0.1%

Order_Date
Categorical

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
15-03-2022
 
1192
03-04-2022
 
1178
13-03-2022
 
1169
26-03-2022
 
1165
24-03-2022
 
1162
Other values (39)
39718 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters455840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12-02-2022
2nd row13-02-2022
3rd row04-03-2022
4th row13-02-2022
5th row14-02-2022

Common Values

ValueCountFrequency (%)
15-03-2022 1192
 
2.6%
03-04-2022 1178
 
2.6%
13-03-2022 1169
 
2.6%
26-03-2022 1165
 
2.6%
24-03-2022 1162
 
2.5%
09-03-2022 1159
 
2.5%
05-04-2022 1156
 
2.5%
05-03-2022 1154
 
2.5%
07-03-2022 1153
 
2.5%
03-03-2022 1150
 
2.5%
Other values (34) 33946
74.5%

Length

2025-08-12T23:09:33.645736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15-03-2022 1192
 
2.6%
03-04-2022 1178
 
2.6%
13-03-2022 1169
 
2.6%
26-03-2022 1165
 
2.6%
24-03-2022 1162
 
2.5%
09-03-2022 1159
 
2.5%
05-04-2022 1156
 
2.5%
05-03-2022 1154
 
2.5%
07-03-2022 1153
 
2.5%
03-03-2022 1150
 
2.5%
Other values (34) 33946
74.5%

Most occurring characters

ValueCountFrequency (%)
2 157314
34.5%
0 110357
24.2%
- 91168
20.0%
3 39507
 
8.7%
1 24436
 
5.4%
4 11268
 
2.5%
5 5421
 
1.2%
6 4968
 
1.1%
7 4191
 
0.9%
8 3926
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 364672
80.0%
Dash Punctuation 91168
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 157314
43.1%
0 110357
30.3%
3 39507
 
10.8%
1 24436
 
6.7%
4 11268
 
3.1%
5 5421
 
1.5%
6 4968
 
1.4%
7 4191
 
1.1%
8 3926
 
1.1%
9 3284
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 91168
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 455840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 157314
34.5%
0 110357
24.2%
- 91168
20.0%
3 39507
 
8.7%
1 24436
 
5.4%
4 11268
 
2.5%
5 5421
 
1.2%
6 4968
 
1.1%
7 4191
 
0.9%
8 3926
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 455840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 157314
34.5%
0 110357
24.2%
- 91168
20.0%
3 39507
 
8.7%
1 24436
 
5.4%
4 11268
 
2.5%
5 5421
 
1.2%
6 4968
 
1.1%
7 4191
 
0.9%
8 3926
 
0.9%

Time_Orderd
Text

Missing 

Distinct176
Distinct (%)0.4%
Missing1731
Missing (%)3.8%
Memory size2.7 MiB
2025-08-12T23:09:33.760910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.1861446
Min length1

Characters and Unicode

Total characters227428
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row21:55
2nd row14:55
3rd row17:30
4th row9:20
5th row19:50
ValueCountFrequency (%)
21:55 461
 
1.1%
17:55 456
 
1.0%
0.833333333 449
 
1.0%
22:20 448
 
1.0%
21:35 446
 
1.0%
19:50 444
 
1.0%
21:15 442
 
1.0%
22:45 438
 
1.0%
21:20 437
 
1.0%
18:35 436
 
1.0%
Other values (166) 39396
89.8%
2025-08-12T23:09:33.906070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 39785
17.5%
1 34999
15.4%
5 30724
13.5%
2 29555
13.0%
0 29411
12.9%
3 21168
9.3%
4 9221
 
4.1%
8 7507
 
3.3%
6 7371
 
3.2%
9 7189
 
3.2%
Other values (2) 10498
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184005
80.9%
Other Punctuation 43423
 
19.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 34999
19.0%
5 30724
16.7%
2 29555
16.1%
0 29411
16.0%
3 21168
11.5%
4 9221
 
5.0%
8 7507
 
4.1%
6 7371
 
4.0%
9 7189
 
3.9%
7 6860
 
3.7%
Other Punctuation
ValueCountFrequency (%)
: 39785
91.6%
. 3638
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
Common 227428
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 39785
17.5%
1 34999
15.4%
5 30724
13.5%
2 29555
13.0%
0 29411
12.9%
3 21168
9.3%
4 9221
 
4.1%
8 7507
 
3.3%
6 7371
 
3.2%
9 7189
 
3.2%
Other values (2) 10498
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 227428
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 39785
17.5%
1 34999
15.4%
5 30724
13.5%
2 29555
13.0%
0 29411
12.9%
3 21168
9.3%
4 9221
 
4.1%
8 7507
 
3.3%
6 7371
 
3.2%
9 7189
 
3.2%
Other values (2) 10498
 
4.6%
Distinct193
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2025-08-12T23:09:34.050535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.2389216
Min length1

Characters and Unicode

Total characters238811
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22:10
2nd row15:05
3rd row17:40
4th row9:30
5th row20:05
ValueCountFrequency (%)
21:30 495
 
1.1%
22:50 474
 
1.0%
22:40 458
 
1.0%
18:40 457
 
1.0%
17:55 456
 
1.0%
21:45 456
 
1.0%
22:25 455
 
1.0%
18:05 454
 
1.0%
23:50 453
 
1.0%
20:50 453
 
1.0%
Other values (183) 40973
89.9%
2025-08-12T23:09:34.237243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
: 42337
17.7%
5 34602
14.5%
0 33947
14.2%
1 33302
13.9%
2 30426
12.7%
3 21418
9.0%
4 10316
 
4.3%
6 7687
 
3.2%
8 7435
 
3.1%
9 7338
 
3.1%
Other values (2) 10003
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192787
80.7%
Other Punctuation 46024
 
19.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 34602
17.9%
0 33947
17.6%
1 33302
17.3%
2 30426
15.8%
3 21418
11.1%
4 10316
 
5.4%
6 7687
 
4.0%
8 7435
 
3.9%
9 7338
 
3.8%
7 6316
 
3.3%
Other Punctuation
ValueCountFrequency (%)
: 42337
92.0%
. 3687
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
Common 238811
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
: 42337
17.7%
5 34602
14.5%
0 33947
14.2%
1 33302
13.9%
2 30426
12.7%
3 21418
9.0%
4 10316
 
4.3%
6 7687
 
3.2%
8 7435
 
3.1%
9 7338
 
3.1%
Other values (2) 10003
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 238811
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
: 42337
17.7%
5 34602
14.5%
0 33947
14.2%
1 33302
13.9%
2 30426
12.7%
3 21418
9.0%
4 10316
 
4.3%
6 7687
 
3.2%
8 7435
 
3.1%
9 7338
 
3.1%
Other values (2) 10003
 
4.2%

Weather_conditions
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing616
Missing (%)1.4%
Memory size2.7 MiB
Fog
7653 
Stormy
7584 
Cloudy
7533 
Sandstorms
7494 
Windy
7422 

Length

Max length10
Median length6
Mean length5.8290562
Min length3

Characters and Unicode

Total characters262121
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFog
2nd rowStormy
3rd rowSandstorms
4th rowSandstorms
5th rowFog

Common Values

ValueCountFrequency (%)
Fog 7653
16.8%
Stormy 7584
16.6%
Cloudy 7533
16.5%
Sandstorms 7494
16.4%
Windy 7422
16.3%
Sunny 7282
16.0%
(Missing) 616
 
1.4%

Length

2025-08-12T23:09:34.287412image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:09:34.342036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
fog 7653
17.0%
stormy 7584
16.9%
cloudy 7533
16.8%
sandstorms 7494
16.7%
windy 7422
16.5%
sunny 7282
16.2%

Most occurring characters

ValueCountFrequency (%)
o 30264
11.5%
y 29821
11.4%
n 29480
11.2%
d 22449
8.6%
S 22360
8.5%
t 15078
 
5.8%
r 15078
 
5.8%
m 15078
 
5.8%
s 14988
 
5.7%
u 14815
 
5.7%
Other values (7) 52710
20.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 217153
82.8%
Uppercase Letter 44968
 
17.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 30264
13.9%
y 29821
13.7%
n 29480
13.6%
d 22449
10.3%
t 15078
6.9%
r 15078
6.9%
m 15078
6.9%
s 14988
6.9%
u 14815
6.8%
g 7653
 
3.5%
Other values (3) 22449
10.3%
Uppercase Letter
ValueCountFrequency (%)
S 22360
49.7%
F 7653
 
17.0%
C 7533
 
16.8%
W 7422
 
16.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 262121
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 30264
11.5%
y 29821
11.4%
n 29480
11.2%
d 22449
8.6%
S 22360
8.5%
t 15078
 
5.8%
r 15078
 
5.8%
m 15078
 
5.8%
s 14988
 
5.7%
u 14815
 
5.7%
Other values (7) 52710
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 262121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 30264
11.5%
y 29821
11.4%
n 29480
11.2%
d 22449
8.6%
S 22360
8.5%
t 15078
 
5.8%
r 15078
 
5.8%
m 15078
 
5.8%
s 14988
 
5.7%
u 14815
 
5.7%
Other values (7) 52710
20.1%

Road_traffic_density
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing601
Missing (%)1.3%
Memory size2.6 MiB
Low
15476 
Jam
14139 
Medium
10945 
High
4423 

Length

Max length6
Median length3
Mean length3.8282685
Min length3

Characters and Unicode

Total characters172207
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJam
2nd rowHigh
3rd rowMedium
4th rowLow
5th rowJam

Common Values

ValueCountFrequency (%)
Low 15476
34.0%
Jam 14139
31.0%
Medium 10945
24.0%
High 4423
 
9.7%
(Missing) 601
 
1.3%

Length

2025-08-12T23:09:34.385857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:09:34.418123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
low 15476
34.4%
jam 14139
31.4%
medium 10945
24.3%
high 4423
 
9.8%

Most occurring characters

ValueCountFrequency (%)
m 25084
14.6%
L 15476
9.0%
o 15476
9.0%
w 15476
9.0%
i 15368
8.9%
J 14139
8.2%
a 14139
8.2%
M 10945
6.4%
e 10945
6.4%
d 10945
6.4%
Other values (4) 24214
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 127224
73.9%
Uppercase Letter 44983
 
26.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 25084
19.7%
o 15476
12.2%
w 15476
12.2%
i 15368
12.1%
a 14139
11.1%
e 10945
8.6%
d 10945
8.6%
u 10945
8.6%
g 4423
 
3.5%
h 4423
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
L 15476
34.4%
J 14139
31.4%
M 10945
24.3%
H 4423
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 172207
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 25084
14.6%
L 15476
9.0%
o 15476
9.0%
w 15476
9.0%
i 15368
8.9%
J 14139
8.2%
a 14139
8.2%
M 10945
6.4%
e 10945
6.4%
d 10945
6.4%
Other values (4) 24214
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172207
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 25084
14.6%
L 15476
9.0%
o 15476
9.0%
w 15476
9.0%
i 15368
8.9%
J 14139
8.2%
a 14139
8.2%
M 10945
6.4%
e 10945
6.4%
d 10945
6.4%
Other values (4) 24214
14.1%

Vehicle_condition
Categorical

High correlation 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2
15031 
1
15028 
0
15005 
3
 
520

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters45584
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
2 15031
33.0%
1 15028
33.0%
0 15005
32.9%
3 520
 
1.1%

Length

2025-08-12T23:09:34.466656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:09:34.500040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 15031
33.0%
1 15028
33.0%
0 15005
32.9%
3 520
 
1.1%

Most occurring characters

ValueCountFrequency (%)
2 15031
33.0%
1 15028
33.0%
0 15005
32.9%
3 520
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45584
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 15031
33.0%
1 15028
33.0%
0 15005
32.9%
3 520
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Common 45584
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 15031
33.0%
1 15028
33.0%
0 15005
32.9%
3 520
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 15031
33.0%
1 15028
33.0%
0 15005
32.9%
3 520
 
1.1%

Type_of_order
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
Snack
11530 
Meal
11456 
Drinks
11321 
Buffet
11277 

Length

Max length6
Median length5
Mean length5.2444279
Min length4

Characters and Unicode

Total characters239062
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSnack
2nd rowMeal
3rd rowDrinks
4th rowBuffet
5th rowSnack

Common Values

ValueCountFrequency (%)
Snack 11530
25.3%
Meal 11456
25.1%
Drinks 11321
24.8%
Buffet 11277
24.7%

Length

2025-08-12T23:09:34.542570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:09:34.576301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
snack 11530
25.3%
meal 11456
25.1%
drinks 11321
24.8%
buffet 11277
24.7%

Most occurring characters

ValueCountFrequency (%)
a 22986
 
9.6%
n 22851
 
9.6%
k 22851
 
9.6%
e 22733
 
9.5%
f 22554
 
9.4%
S 11530
 
4.8%
c 11530
 
4.8%
M 11456
 
4.8%
l 11456
 
4.8%
D 11321
 
4.7%
Other values (6) 67794
28.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 193478
80.9%
Uppercase Letter 45584
 
19.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 22986
11.9%
n 22851
11.8%
k 22851
11.8%
e 22733
11.7%
f 22554
11.7%
c 11530
6.0%
l 11456
5.9%
r 11321
5.9%
i 11321
5.9%
s 11321
5.9%
Other values (2) 22554
11.7%
Uppercase Letter
ValueCountFrequency (%)
S 11530
25.3%
M 11456
25.1%
D 11321
24.8%
B 11277
24.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 239062
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 22986
 
9.6%
n 22851
 
9.6%
k 22851
 
9.6%
e 22733
 
9.5%
f 22554
 
9.4%
S 11530
 
4.8%
c 11530
 
4.8%
M 11456
 
4.8%
l 11456
 
4.8%
D 11321
 
4.7%
Other values (6) 67794
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 239062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 22986
 
9.6%
n 22851
 
9.6%
k 22851
 
9.6%
e 22733
 
9.5%
f 22554
 
9.4%
S 11530
 
4.8%
c 11530
 
4.8%
M 11456
 
4.8%
l 11456
 
4.8%
D 11321
 
4.7%
Other values (6) 67794
28.4%

Type_of_vehicle
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
motorcycle
26429 
scooter
15273 
electric_scooter
3814 
bicycle
 
68

Length

Max length16
Median length10
Mean length9.4923877
Min length7

Characters and Unicode

Total characters432701
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmotorcycle
2nd rowmotorcycle
3rd rowscooter
4th rowmotorcycle
5th rowscooter

Common Values

ValueCountFrequency (%)
motorcycle 26429
58.0%
scooter 15273
33.5%
electric_scooter 3814
 
8.4%
bicycle 68
 
0.1%

Length

2025-08-12T23:09:34.623844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:09:34.657238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
motorcycle 26429
58.0%
scooter 15273
33.5%
electric_scooter 3814
 
8.4%
bicycle 68
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 91032
21.0%
c 79709
18.4%
e 53212
12.3%
t 49330
11.4%
r 49330
11.4%
l 30311
 
7.0%
y 26497
 
6.1%
m 26429
 
6.1%
s 19087
 
4.4%
i 3882
 
0.9%
Other values (2) 3882
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 428887
99.1%
Connector Punctuation 3814
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 91032
21.2%
c 79709
18.6%
e 53212
12.4%
t 49330
11.5%
r 49330
11.5%
l 30311
 
7.1%
y 26497
 
6.2%
m 26429
 
6.2%
s 19087
 
4.5%
i 3882
 
0.9%
Connector Punctuation
ValueCountFrequency (%)
_ 3814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 428887
99.1%
Common 3814
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 91032
21.2%
c 79709
18.6%
e 53212
12.4%
t 49330
11.5%
r 49330
11.5%
l 30311
 
7.1%
y 26497
 
6.2%
m 26429
 
6.2%
s 19087
 
4.5%
i 3882
 
0.9%
Common
ValueCountFrequency (%)
_ 3814
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 432701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 91032
21.0%
c 79709
18.4%
e 53212
12.3%
t 49330
11.4%
r 49330
11.4%
l 30311
 
7.0%
y 26497
 
6.1%
m 26429
 
6.1%
s 19087
 
4.4%
i 3882
 
0.9%
Other values (2) 3882
 
0.9%

multiple_deliveries
Categorical

Missing 

Distinct4
Distinct (%)< 0.1%
Missing993
Missing (%)2.2%
Memory size2.6 MiB
1.0
28151 
0.0
14094 
2.0
 
1985
3.0
 
361

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters133773
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 28151
61.8%
0.0 14094
30.9%
2.0 1985
 
4.4%
3.0 361
 
0.8%
(Missing) 993
 
2.2%

Length

2025-08-12T23:09:34.720771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:09:34.760919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 28151
63.1%
0.0 14094
31.6%
2.0 1985
 
4.5%
3.0 361
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 58685
43.9%
. 44591
33.3%
1 28151
21.0%
2 1985
 
1.5%
3 361
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 89182
66.7%
Other Punctuation 44591
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58685
65.8%
1 28151
31.6%
2 1985
 
2.2%
3 361
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 44591
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 133773
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58685
43.9%
. 44591
33.3%
1 28151
21.0%
2 1985
 
1.5%
3 361
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 133773
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58685
43.9%
. 44591
33.3%
1 28151
21.0%
2 1985
 
1.5%
3 361
 
0.3%

Festival
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing228
Missing (%)0.5%
Memory size89.2 KiB
False
44460 
True
 
896
(Missing)
 
228
ValueCountFrequency (%)
False 44460
97.5%
True 896
 
2.0%
(Missing) 228
 
0.5%
2025-08-12T23:09:34.791971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

City
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing1200
Missing (%)2.6%
Memory size3.0 MiB
Metropolitian
34087 
Urban
10133 
Semi-Urban
 
164

Length

Max length13
Median length13
Mean length11.162491
Min length5

Characters and Unicode

Total characters495436
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMetropolitian
2nd rowMetropolitian
3rd rowMetropolitian
4th rowMetropolitian
5th rowMetropolitian

Common Values

ValueCountFrequency (%)
Metropolitian 34087
74.8%
Urban 10133
 
22.2%
Semi-Urban 164
 
0.4%
(Missing) 1200
 
2.6%

Length

2025-08-12T23:09:34.839646image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-12T23:09:34.887071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
metropolitian 34087
76.8%
urban 10133
 
22.8%
semi-urban 164
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 68338
13.8%
t 68174
13.8%
o 68174
13.8%
r 44384
9.0%
a 44384
9.0%
n 44384
9.0%
e 34251
6.9%
M 34087
6.9%
p 34087
6.9%
l 34087
6.9%
Other values (5) 21086
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 450724
91.0%
Uppercase Letter 44548
 
9.0%
Dash Punctuation 164
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 68338
15.2%
t 68174
15.1%
o 68174
15.1%
r 44384
9.8%
a 44384
9.8%
n 44384
9.8%
e 34251
7.6%
p 34087
7.6%
l 34087
7.6%
b 10297
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
M 34087
76.5%
U 10297
 
23.1%
S 164
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 495272
> 99.9%
Common 164
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 68338
13.8%
t 68174
13.8%
o 68174
13.8%
r 44384
9.0%
a 44384
9.0%
n 44384
9.0%
e 34251
6.9%
M 34087
6.9%
p 34087
6.9%
l 34087
6.9%
Other values (4) 20922
 
4.2%
Common
ValueCountFrequency (%)
- 164
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495436
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 68338
13.8%
t 68174
13.8%
o 68174
13.8%
r 44384
9.0%
a 44384
9.0%
n 44384
9.0%
e 34251
6.9%
M 34087
6.9%
p 34087
6.9%
l 34087
6.9%
Other values (5) 21086
 
4.3%

Time_taken (min)
Real number (ℝ)

Distinct45
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.293963
Minimum10
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-08-12T23:09:34.948919image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13
Q119
median26
Q332
95-th percentile44
Maximum54
Range44
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3842977
Coefficient of variation (CV)0.35689933
Kurtosis-0.31081839
Mean26.293963
Median Absolute Deviation (MAD)7
Skewness0.48608612
Sum1198584
Variance88.065044
MonotonicityNot monotonic
2025-08-12T23:09:35.024160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
26 2121
 
4.7%
25 2050
 
4.5%
27 1976
 
4.3%
28 1965
 
4.3%
29 1956
 
4.3%
19 1824
 
4.0%
15 1810
 
4.0%
18 1765
 
3.9%
16 1706
 
3.7%
17 1696
 
3.7%
Other values (35) 26715
58.6%
ValueCountFrequency (%)
10 750
1.6%
11 757
1.7%
12 746
1.6%
13 716
 
1.6%
14 739
1.6%
15 1810
4.0%
16 1706
3.7%
17 1696
3.7%
18 1765
3.9%
19 1824
4.0%
ValueCountFrequency (%)
54 91
 
0.2%
53 100
 
0.2%
52 79
 
0.2%
51 94
 
0.2%
50 72
 
0.2%
49 280
0.6%
48 277
0.6%
47 295
0.6%
46 274
0.6%
45 241
0.5%

distance (km)
Real number (ℝ)

Distinct684
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.321338
Minimum1.47
Maximum19692.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size356.3 KiB
2025-08-12T23:09:35.102385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.47
5-th percentile1.54
Q14.66
median9.26
Q313.76
95-th percentile20.18
Maximum19692.67
Range19691.2
Interquartile range (IQR)9.1

Descriptive statistics

Standard deviation1099.8392
Coefficient of variation (CV)11.073544
Kurtosis220.09134
Mean99.321338
Median Absolute Deviation (MAD)4.54
Skewness14.458805
Sum4527463.9
Variance1209646.2
MonotonicityNot monotonic
2025-08-12T23:09:35.193262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.66 852
 
1.9%
1.55 846
 
1.9%
1.51 785
 
1.7%
1.53 628
 
1.4%
3.11 596
 
1.3%
6.21 581
 
1.3%
3.06 580
 
1.3%
12.42 578
 
1.3%
7.76 575
 
1.3%
20.18 568
 
1.2%
Other values (674) 38995
85.5%
ValueCountFrequency (%)
1.47 101
 
0.2%
1.49 386
0.8%
1.5 44
 
0.1%
1.51 785
1.7%
1.52 273
 
0.6%
1.53 628
1.4%
1.54 337
 
0.7%
1.55 846
1.9%
1.56 346
0.8%
1.57 327
 
0.7%
ValueCountFrequency (%)
19692.67 1
< 0.1%
19688 1
< 0.1%
19683.69 1
< 0.1%
19677.18 1
< 0.1%
19070.41 1
< 0.1%
19070.34 1
< 0.1%
19069.16 1
< 0.1%
19068.25 1
< 0.1%
19067.13 1
< 0.1%
19066.15 1
< 0.1%

Interactions

2025-08-12T23:09:31.381821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.252202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.709110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.106135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.536873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.967099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.506869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.930179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.448364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.303516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.761657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.168548image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.594041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.019655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.548590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.992491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.500145image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.384368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.799524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.215389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.648493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.173895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.599906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.051852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.557458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.437205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.857733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.264852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.698941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.233271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.649445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.103822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.601539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.489853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.906475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.322458image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.749397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.290279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.703248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.155920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.666381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.549773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.949499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.371475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.804155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.340061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.766291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.216850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.723159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.598948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.999933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.426725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.857087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.394176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.814438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.264155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.766468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:28.648281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.060487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.479526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:29.905951image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.440571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:30.864994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-12T23:09:31.316384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-12T23:09:35.265844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CityDelivery_location_latitudeDelivery_location_longitudeDelivery_person_AgeDelivery_person_RatingsFestivalOrder_DateRestaurant_latitudeRestaurant_longitudeRoad_traffic_densityTime_taken (min)Type_of_orderType_of_vehicleVehicle_conditionWeather_conditionsdistance (km)multiple_deliveries
City1.0000.0010.0010.0620.0490.1050.0520.0000.0040.0750.2780.0070.0360.0630.0370.0000.130
Delivery_location_latitude0.0011.0000.1220.004-0.0100.0000.1810.9730.1160.0180.0300.0000.0110.0000.0000.0050.009
Delivery_location_longitude0.0010.1221.0000.008-0.0080.0000.1380.1110.9880.0010.0280.0000.0090.0000.0000.0600.004
Delivery_person_Age0.0620.0040.0081.000-0.0960.0690.0000.0030.0060.0000.3120.0080.2430.5770.007-0.0010.079
Delivery_person_Ratings0.049-0.010-0.008-0.0961.0000.0680.053-0.007-0.0040.078-0.2940.0000.2470.5820.082-0.0670.091
Festival0.1050.0000.0000.0690.0681.0000.1090.0010.0080.1260.4250.0000.0560.1000.0700.0070.208
Order_Date0.0520.1810.1380.0000.0530.1091.0000.1390.0990.2130.1240.0000.0120.0070.0060.0400.098
Restaurant_latitude0.0000.9730.1110.003-0.0070.0010.1391.0000.1220.0060.0150.0000.0650.1640.000-0.0820.011
Restaurant_longitude0.0040.1160.9880.006-0.0040.0080.0990.1221.0000.0020.0080.0000.1090.2510.000-0.0160.000
Road_traffic_density0.0750.0180.0010.0000.0780.1260.2130.0060.0021.0000.2650.0000.0000.0060.0000.0000.109
Time_taken (min)0.2780.0300.0280.312-0.2940.4250.1240.0150.0080.2651.0000.0000.1050.1820.1390.3140.337
Type_of_order0.0070.0000.0000.0080.0000.0000.0000.0000.0000.0000.0001.0000.0000.0030.0020.0000.007
Type_of_vehicle0.0360.0110.0090.2430.2470.0560.0120.0650.1090.0000.1050.0001.0000.4570.0000.0900.047
Vehicle_condition0.0630.0000.0000.5770.5820.1000.0070.1640.2510.0060.1820.0030.4571.0000.0000.2090.075
Weather_conditions0.0370.0000.0000.0070.0820.0700.0060.0000.0000.0000.1390.0020.0000.0001.0000.0000.068
distance (km)0.0000.0050.060-0.001-0.0670.0070.040-0.082-0.0160.0000.3140.0000.0900.2090.0001.0000.000
multiple_deliveries0.1300.0090.0040.0790.0910.2080.0980.0110.0000.1090.3370.0070.0470.0750.0680.0001.000

Missing values

2025-08-12T23:09:31.879730image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-12T23:09:32.016220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-08-12T23:09:32.242650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IDDelivery_person_IDDelivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderdTime_Order_pickedWeather_conditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken (min)distance (km)
00xcdcdDEHRES17DEL0136.04.230.32796878.04610630.39796878.11610612-02-202221:5522:10FogJam2Snackmotorcycle3.0NoMetropolitian4610.28
10xd987KOCRES16DEL0121.04.710.00306476.30758910.04306476.34758913-02-202214:5515:05StormyHigh1Mealmotorcycle1.0NoMetropolitian236.24
20x2784PUNERES13DEL0323.04.718.56245073.91661918.65245074.00661904-03-202217:3017:40SandstormsMedium1Drinksscooter1.0NoMetropolitian2113.79
30xc8b6LUDHRES15DEL0234.04.330.89958475.80934630.91958475.82934613-02-20229:209:30SandstormsLow0Buffetmotorcycle0.0NoMetropolitian202.93
40xdb64KNPRES14DEL0224.04.726.46350480.37292926.59350480.50292914-02-202219:5020:05FogJam1Snackscooter1.0NoMetropolitian4119.40
50x3af3MUMRES15DEL0329.04.519.17626972.83672119.26626972.92672102-04-202220:2520:35SandstormsJam2Buffetelectric_scooter1.0NoMetropolitian2013.76
60x3aabMYSRES01DEL0135.04.012.31107276.65487812.35107276.69487801-03-202214:5515:10WindyHigh1Mealscooter1.0NoMetropolitian336.22
70x689bPUNERES20DEL0133.04.218.59271873.77357218.70271873.88357216-03-202220:3020:40SandstormsJam2Snackmotorcycle1.0NoMetropolitian4016.85
80x6f67HYDRES14DEL0134.04.917.42622878.40749517.49622878.47749520-03-202220:4020:50CloudyJam0SnackmotorcycleNaNNoMetropolitian4110.76
90xc9cfKOLRES15DEL0321.04.722.55267288.35288522.58267288.38288515-02-202221:1521:30WindyJam0Mealmotorcycle1.0NoUrban154.54
IDDelivery_person_IDDelivery_person_AgeDelivery_person_RatingsRestaurant_latitudeRestaurant_longitudeDelivery_location_latitudeDelivery_location_longitudeOrder_DateTime_OrderdTime_Order_pickedWeather_conditionsRoad_traffic_densityVehicle_conditionType_of_orderType_of_vehiclemultiple_deliveriesFestivalCityTime_taken (min)distance (km)
455740x5193MYSRES13DEL0236.04.812.31097276.65926412.44097276.78926418-03-202221:1021:20SunnyJam2Drinkselectric_scooter1.0NoUrban2920.21
455750xa333CHENRES08DEL0237.04.813.02239480.24243913.04239480.26243905-04-20229:359:50SandstormsLow2Drinkselectric_scooter0.0NoMetropolitian203.10
455760xc9abKNPRES03DEL0130.04.226.46900380.31634426.53900380.38634414-02-202218:1018:25CloudyMedium1Snackmotorcycle2.0YesMetropolitian4210.45
455770x4e21BANGRES16DEL0328.04.913.02919877.57099713.05919877.60099730-03-202221:550.916666667SandstormsJam1Mealscooter1.0NoMetropolitian294.66
455780x1178RANCHIRES16DEL0135.04.223.37129285.32787223.48129285.43787208-03-202221:4521:55WindyJam2Drinksmotorcycle1.0NoMetropolitian3316.60
455790x7c09JAPRES04DEL0130.04.826.90232875.79425726.91232875.80425724-03-202211:3511:45WindyHigh1Mealmotorcycle0.0NoMetropolitian321.49
455800xd641AGRRES16DEL0121.04.60.0000000.0000000.0700000.07000016-02-202219:5520:10WindyJam0Buffetmotorcycle1.0NoMetropolitian3611.01
455810x4f8dCHENRES08DEL0330.04.913.02239480.24243913.05239480.27243911-03-202223:5024:05:00CloudyLow1Drinksscooter0.0NoMetropolitian164.66
455820x5eeeCOIMBRES11DEL0120.04.711.00175376.98624111.04175377.02624107-03-202213:3513:40CloudyHigh0Snackmotorcycle1.0NoMetropolitian266.23
455830x5fb2RANCHIRES09DEL0223.04.923.35105885.32573123.43105885.40573102-03-202217:1017:15FogMedium2Snackscooter1.0NoMetropolitian3612.07